Abstract: The paper describes the use of subspace based
identification methods for auto tuning of a state space control system.
The plant is an unstable but self balancing transport robot. Because
of the unstable character of the process it has to be identified
from closed loop input-output data. Based on the identified model
a state space controller combined with an observer is calculated. The
subspace identification algorithm and the controller design procedure
is combined to a auto tuning method. The capability of the approach
was verified in a simulation experiments under different process
conditions.
Abstract: An optimal control of Reverse Osmosis (RO) plant is
studied in this paper utilizing the auto tuning concept in conjunction
with PID controller. A control scheme composing an auto tuning
stochastic technique based on an improved Genetic Algorithm (GA) is
proposed. For better evaluation of the process in GA, objective
function defined newly in sense of root mean square error has been
used. Also in order to achieve better performance of GA, more
pureness and longer period of random number generation in operation
are sought. The main improvement is made by replacing the uniform
distribution random number generator in conventional GA technique
to newly designed hybrid random generator composed of Cauchy
distribution and linear congruential generator, which provides
independent and different random numbers at each individual steps in
Genetic operation. The performance of newly proposed GA tuned
controller is compared with those of conventional ones via simulation.
Abstract: One of the most basic functions of control engineers is
tuning of controllers. There are always several process loops in the
plant necessitate of tuning. The auto tuned Proportional Integral
Derivative (PID) Controllers are designed for applications where
large load changes are expected or the need for extreme accuracy and
fast response time exists. The algorithm presented in this paper is
used for the tuning PID controller to obtain its parameters with a
minimum computing complexity. It requires continuous analysis of
variation in few parameters, and let the program to do the plant test
and calculate the controller parameters to adjust and optimize the
variables for the best performance. The algorithm developed needs
less time as compared to a normal step response test for continuous
tuning of the PID through gain scheduling.